Short Selling's Impact on Earnings Management: An Experiment

The provided document is a research paper titled "Short Selling and Earnings Management: A Controlled Experiment" published in the Journal of Finance. The study, conducted during the SEC's pilot program (2005-2007) where some stocks were exempt from short-sale price tests, investigates the impact of short selling on corporate financial reporting. The research reveals that pilot firms exhibited decreased discretionary accruals and a lower likelihood of marginally beating earnings targets. Moreover, these firms were more likely to be caught for pre-existing fraud and showed improved stock price efficiency in incorporating earnings information. The findings suggest that short selling, or the prospect of it, acts as a constraint on earnings management, aids in fraud detection, and enhances price efficiency. The paper contributes to the literature by demonstrating the influence of short selling on financial reporting, identifying it as a determinant of earnings management, highlighting its role in improving price efficiency, and adding to the policy debate surrounding the benefits and costs of short selling.

THE JOURNAL OF FINANCE • VOL. LXXI, NO. 3 • JUNE 2016
Short Selling and Earnings Management:
A Controlled Experiment
VIVIAN W. FANG, ALLEN H. HUANG, and JONATHAN M. KARPOFF ∗
ABSTRACT
During 2005 to 2007,the SEC ordered a pilot program in which one-third of the
Russell 3000 index were arbitrarily chosen as pilot stocks and exempted from short-
sale price tests. Pilot firms’ discretionary accruals and likelihood of marginally beating
earnings targets decrease during this period, and revert to pre-experiment levels when
the program ends. After the program starts, pilot firms are more likely to be caught
for fraud initiated before the program,and their stock returns better incorporate
earnings information. These results indicate that short selling, or its prospect, curbs
earnings management, helps detect fraud, and improves price efficiency.
PREVIOUS RESEARCH SHOWS that short sellers can identify earnings manipulation
and fraud before they are publicly revealed.1 But this is for earnings manip-
ulation that has already taken place. Might short selling also constrain firms’
incentives to manipulate or misrepresent earnings in the first place? That is,
does the prospect of short selling help improve the quality of firms’financial
reporting?
In this paper we exploit a randomized experiment that allows us to address
this question.In July 2004, the Securities and Exchange Commission (SEC)
adopted a new regulation governing short-selling activities in the U.S. equity
markets–Regulation SHO.Regulation SHO contained a Rule 202T pilot pro-
gram in which stocks in the Russell 3000 index were ranked by trading volume
∗Fang is with the University of Minnesota.Huang is with the Hong Kong University of Sci-
ence and Technology.Karpoff is with the University of Washington.We are grateful for helpful
comments from two anonymous referees, an anonymous Associate Editor, Kenneth Singleton (the
Editor), Vikas Agarwal, Mark Chen, John Core, Hemang Desai, Jarrad Harford, Adam Kolasin-
ski, Craig Lewis, Paul Ma, Scott Richardson,Ed Swanson, Jake Thornock, Wendy Wilson,and
seminar participants at the Cheung Kong Graduate School of Business,Peking University, the
SEC/Maryland Conference on the Regulation of Financial Markets, the CEAR/GSU Finance Sym-
posium on Corporate ControlMechanisms and Risk, the FARS Midyear Meeting, the HKUST
Accounting Symposium,the CFEA Conference,and the UC Berkeley Multi-disciplinary Confer-
ence on Fraud and Misconduct.We are grateful to Russell Investments for providing the list of
2004 Russell 3000 index firms, and to Jerry Martin for providing the KKLM data on financial mis-
representation. Huang gratefully acknowledges financial support from a grant from the Research
Grants Council of the HKSAR, China (Project No. HKUST691213).
1 See Dechow, Sloan, and Sweeney (1996), Christophe, Ferri, and Angel (2004), Efendi, Kinney,
and Swanson (2005),Desai, Krishnamurthy, and Venkataraman (2006),and Karpoff and Lou
(2010).
DOI: 10.1111/jofi.12369
1251

1252 The Journal of FinanceR
within each exchange and every third one was designated as a pilot stock.
From May 2, 2005 to August 6, 2007, pilot stocks were exempted from short-
sale price tests, including the tick test for exchange-listed stocks and the bid
test for NASDAQ National Market (NASDAQ-NM) stocks.2
The pilot program creates an idealsetting to examine the effect ofshort
selling on corporate financial reporting decisions, for three reasons. First, the
exemption from short-sale price tests decreased the cost ofshort selling in
the pilot stocks relative to the nonpilot stocks (SEC (2007), Diether, Lee, and
Werner (2009)). The pilot program thus eliminates the need to directly estimate
short-selling costs, a notoriously difficult task (Lamont (2012)). Rather, we use
the fact that the prospect of short selling increased for pilot firms relative to
nonpilot firms during the program. Second, the pilot program represents a truly
exogenous shock to the cost of selling short in the affected firms. We identify no
evidence that the firms themselves lobbied for the pilot program, or that any
individual firm could know it would be in the pilot group until the program was
announced. Third, the pilot program had specific beginning and ending dates,
facilitating difference-in-differences (hereafter, DiD) analysis of the impact of
short-selling costs on firms’financial reporting. In particular, the anticipated
ending date allows us to investigate whether the effects of the pilot program
reversed when it ended – an important check on the internal validity of the
DiD tests (e.g., Roberts and Whited (2013)).
We begin by verifying that pilot firms represent a random draw from the Rus-
sell 3000 population. In the fiscal year before the pilot program, pilot and non-
pilot firms are similar in size, growth, investment, profitability, leverage, and
dividend payout. Although the two groups of firms also exhibit similar levels of
discretionary accruals before the program, pilot firms significantly reduce their
signed discretionary accruals once the program starts.3 After the program ends,
pilot firms’discretionary accruals revert to pre-program levels.The nonpilot
firms, meanwhile, show no significant change in discretionary accruals around
the pilot program. Our point estimates indicate that performance-matched dis-
cretionary accruals, as a percentage of assets, are one percentage point lower
for pilot firms than for nonpilot firms during the pilot program compared to
the pre-pilot period.This corresponds to 7.4% ofthe standard deviation of
discretionary accruals in our sample.
We also examine the pilot program’s effect on two alternative measures of
earnings management. First, we find that the likelihood of beating the analyst
2 The pilot program was originally scheduled to commence on January 3,2005,and end on
December 31,2005 (Securities Exchange Act Release No.50104,July 28, 2004).However,the
SEC postponed the commencement date to May 2, 2005 (Securities and Exchange Act Release No.
50747, November 29, 2004) and extended the end date to August 6, 2007 (Securities and Exchange
Act Release No. 53684, April 20, 2006). Before the pilot program ran its entire course, the SEC
eliminated short-sale price tests for all exchange-listed stocks on July 6, 2007 (Securities Exchange
Act of 1934 Release No. 34-55970, July 3, 2007).
3 Following the literature (e.g., Kothari, Leone, and Wasley (2005)), we measure discretionary ac-
cruals as the difference between actual accruals and a benchmark estimated within each industry-
year. Details are provided in Section II.C.

Short Selling and Earnings Management 1253
consensus forecast by up to one cent is 1.8 percentage points lower for the pilot
firms than for the nonpilot firms during the pilot program compared to the pre-
pilot period. This represents 11.1% of the unconditional likelihood of meeting
or just beating the analyst consensus forecast in our sample.Similarly, the
likelihood of meeting or just beating the firm’s quarterly earnings per share
(EPS) in the same quarter of the prior year is 0.8 percentage points lower
for the pilot firms during the pilot program compared to the pre-pilot period,
representing 14.2% of the unconditional likelihood.Second,we find that the
likelihood of being classified as a misstating firm,based on the F-score of
Dechow et al. (2011), is significantly lower for the pilot firms during the pilot
period compared to the pre-pilot period. Combined with our results regarding
discretionary accruals, these results indicate that pilot firms decrease earnings
management during the pilot program.
We consider several alternative interpretations for the patterns we observe
in discretionary accruals. One possibility is that pilot firms’ discretionary accru-
als reflect changes in their growth, investment, or equity issuance, as Grullon,
Michenaud, and Weston (2015) document a significant reduction in financially
constrained pilot firms’investment and equity issuance during the pilot pro-
gram. We consider several ways to control for firm growth and investment, both
in the construction of our discretionary accruals measures and in the multivari-
ate tests. None of these controls have a material effect on our main findings.
We also find that the pilot firms’ investment levels do not follow a pattern that
would explain the changes in their discretionary accruals during and after the
pilot program. Regarding the possible impact of equity issuance, we find that
pilot firms’discretionary accruals pattern is similar between firms that do not
seek to issue equity and the overall sample.These results indicate that the
effect of the pilot program on discretionary accruals is unlikely to be explained
by changes in pilot firms’growth, investment, or equity issuance around the
program.
Another possible explanation is that managers of the pilot firms decreased
earnings management because of a general increase in the attention investors
paid to these firms.Using three measures of market attention,however,we
do not find that pilot firms were subject to greater attention during the pilot
program.In multivariate DiD tests, the market attention measures are not
significantly related to discretionary accruals,nor do they affect our main
findings regarding discretionary accruals.
The most plausible interpretation of our results is that the pilot program
reduced the cost of short selling sufficiently among the pilot firms to increase
potential short sellers’ monitoring activities, and that the increased monitoring
induced a decrease in these firms’earnings management.4 We conduct three
additional tests to further probe this interpretation. First, we find that, among
the pilot firms during the pilot program, short selling is positively related to
signed discretionary accruals. Second, we find that short interest increases in
4 Throughout this paper, we use “potentialshort sellers” or “short sellers” to refer to both
investors who may take new short positions and investors with existing short positions.

1254 The Journal of FinanceR
months in which firms are later revealed to have engaged in financial misrepre-
sentation during our sample period. And third, we find that, among firms that
previously initiated financial fraud,pilot firms are more likely to get caught
than control firms after the pilot period started.We also find that the un-
conditional likelihood of pilot firms being caught for financial fraud converges
monotonically toward that of nonpilot firms as we sequentially include cases of
fraud initiated after the pilot program begins. This result is consistent with the
argument that pilot firms’conditional likelihood of being caught for any fraud
they commit is higher during the program, and with our main finding that pilot
firms endogenously adjust by decreasing earnings manipulation after the pilot
program begins.
Finally, we examine the implications of the pilot program for price efficiency
through its effect on firms’ reporting practices. We show that the coefficients of
pilot firms’ current returns on future earnings increase during the pilot period.
Among firms announcing particularly negative earnings surprises,the well-
documented post-earnings announcement drift (PEAD)disappears for pilot
firms during the period, while it remains significant for nonpilot firms. These
results indicate that the reduction in pilot firms’ earnings management during
the pilot program corresponds to an increase in the efficiency of their stock
prices as their stock returns better incorporate earnings information.
The above findings make four contributions to the literature. First, they show
that an increase in the prospect of short selling has a significant effect on firms’
financial reporting. This result demonstrates one avenue through which trad-
ing in secondary financial markets affects firms’decisions.5 Second, our find-
ings identify a new determinant of earnings management, namely, short-sale
constraints, adding to the factors identified in prior research (for a review, see
Dechow, Ge, and Schrand (2010)). Third, our results indicate that the prospect
of short selling improves price efficiency not only by facilitating the flow of
private information into prices (e.g., Miller (1977), Harrison and Kreps (1978),
Chang, Cheng, and Yu (2007), Boehmer and Wu (2013)), but also by decreasing
managers’tendency to manage earnings. And fourth, our findings contribute
to the policy debate on the benefits and costs of short selling. Previous research
demonstrates that short sellers are good at identifying the overpriced shares
of firms that have manipulated earnings, and short sellers’ trading accelerates
the discovery of financial misconduct.6 Our results indicate that the prospect
of short selling conveys additional external benefits to investors by improving
financial reporting quality and stock price efficiency in general,even among
firms not charged with financial reporting violations.
5 See Bond, Edmans, and Goldstein (2012) for a survey of research on the real effects of financial
markets. For example, Karpoff and Rice (1989) and Fang, Noe, and Tice (2009) examine the effect
of stock liquidity on firm performance, Fang, Tian, and Tice (2014) examine the effect of liquidity
on innovation,and Grullon, Michenaud, and Weston (2015) examine the effect of short-selling
constraints on investment and equity issuance.
6 See the references in footnote 1. To be sure, other studies have noted the potential dark side of
short selling, as manipulative short selling could reduce price efficiency (e.g., Gerard and Nanda
(1993), Henry and Koski (2010)).

Short Selling and Earnings Management 1255
This paper is organized as follows. Section I describes short-sale price tests in
the U.S. equity markets, how they can affect firms’tendency to manage earn-
ings, and related research.Section II describes the data.Section III reports
tests of the effect of Regulation SHO’s pilot program on firms’earnings man-
agement. Section IV examines whether short sellers actually increased their
scrutiny of the pilot stocks during the pilot program by comparing the prob-
ability of fraud detection between pilot and nonpilot firms. Section V reports
on tests that examine whether the pilot program coincided with an increase
in the efficiency of pilot firms’stock prices with respect to earnings.Finally,
Section VI concludes.
I. Short-Sale Price Tests, Their Effect on Earnings Management, and
Related Research
A. Short-Sale Price Tests in U.S. Equity Markets
Short-sale price tests were initially introduced in the U.S. equity markets in
the 1930s, ostensibly to avoid bear raids by short sellers in declining markets.
The NYSE adopted an uptick rule in 1935,which was replaced in 1938 by a
stricter SEC rule, Rule 10a-1,also known as the “tick test.” The latter rule
mandates that a short sale can only occur at a price above the most recently
traded price (plus tick) or at the most recently traded price if that price exceeds
the last different price (zero-plus tick).7 In 1994, the National Association of
Securities Dealers (NASD) adopted its own price test (the “bid test”) under Rule
3350. Rule 3350 requires that a short sale occur at a price one penny above the
bid price if the bid is a downtick from the previous bid.8
To facilitate research on the effects ofshort-sale price tests on financial
markets, the SEC initiated a pilot program under Rule 202T of Regulation
SHO in July 2004. Under the pilot program, every third stock in the Russell
3000 index ranked by trading volume within each exchange was selected as a
pilot stock. From May 2, 2005, to August 6, 2007, pilot stocks were exempted
from short-sale price tests. The program effectively ended one month early on
July 6, 2007, when the SEC eliminated short-sale price tests for all exchange-
listed stocks including the nonpilot stocks.
The decision to eliminate all short-sale price tests prompted a huge back-
lash from managers and politicians.In 2008, NYSE Euronext commissioned
Opinion Research Corporation (2008)to conduct a study to seek corporate
7 Narrow exceptions apply, as specified in SEC’s Rule 10a-1, section (e).
8 Rule 3350 applies to NASDAQ National Market (NASDAQ-NM or NNM) securities. Securities
traded in the OTC markets, including NASDAQ Small Cap, OTCBB, and OTC Pink Sheets, are
exempted. When NASDAQ became a national listed exchange in August 2006, NASD Rule 3350
was replaced by NASDAQ Rule 3350 for NASDAQ Global Market securities (formerly NASDAQ-
NM securities) traded on NASDAQ, and NASD Rule 5100 for NASDAQ-NM securities traded over
the counter. The NASDAQ switched from fractional pricing to decimal pricing over the March 12,
2001 to April 9, 2001 period. Prior to decimalization, Rule 3350 required a short sale to occur at a
price 1/8th of a dollar (if before June 2, 1997) or 1/16th of a dollar (if after June 2, 1997) above the
bid.

1256 The Journal of FinanceR
issuers’views on short selling. Fully 85% of the surveyed corporate managers
favored reinstituting the short-sale price tests “as soon as practical,” indicating
that managers are aware of and sensitive to the impact of eliminating price
tests on the potential amount of short selling in their firms. The former state
banking superintendent of New York argued that the SEC’s repeal of the price
tests added to market volatility, especially in down markets.9 The Wall Street
Journal argued that SEC (2007) was too biased to evaluate the short-sale price
tests fairly.10 Wachtell, Lipton, Rosen & Katz, a well-known law firm, argued
that the uptick rule should be reinstated immediately,and three members
of Congress introduced a bill (H.R.6517) requiring the SEC to reinstate the
uptick rule. Presidential candidate Sen. John McCain blamed the SEC for the
recent financial turmoil by “turning our markets into a casino,” in part because
of the increased prospect of short sales, and called for the SEC’s chairman to
be dismissed. In response to this pressure, the SEC partially reversed course
and restored a modified uptick rule on February 24, 2010. Under the new rule,
price tests are triggered when a security’s price declines by 10% or more from
the previous day’s closing price. This policy reversal drew sharp criticism itself,
this time from hedge funds and short sellers.11
B. The Impact of the Pilot Program on Earnings Management
The strong public reactions to changes in the uptick rule indicate that the
rule is important to investors, managers, and politicians. Consistent with prac-
titioners’perception, most prior research indicates that short-sale price tests
impose meaningfulconstraints on short selling,an assumption we examine
further in the next section.12 In this section, we draw from prior studies to
construct our main hypothesis on how changes in the cost of short selling due
to the removal of short-sale price tests, and the corresponding changes in the
prospect of short selling,affect a manager’s tendency to engage in earnings
management.
Previous research indicates that executives have incentives to distort their
firms’ reported financial performance to bolster their compensation,gains
through stock sales, job security, operational flexibility, or control.13 These find-
ings imply that managers can earn a personal benefit from managing earnings
9 Gretchen Morgenson, “Why the roller coaster seems wilder,” The New York Times, August 26,
2007, page 31.
10 See “There’s a better way to prevent bear raids,” The Wall Street Journal, November 18, 2008,
page A19.
11 See “Hedge funds slam short-sale rule,” available at http://dealbook.nytimes.com/2010/02/
25/hedge-funds-slam-short-sale-rule/? r=0.
12 See, for example, McCormick and Reilly (1996), Angel (1997), Alexander and Peterson (1999,
2008),SEC (2007),and Diether,Lee, and Werner (2009).For a contradictory finding,see Ferri,
Christophe, and Angel (2004).
13 For evidence regarding compensation motives, see Bergstresser and Philippon (2006), Burns
and Kedia (2006), and Efendi, Srivastava, and Swanson (2007); for stock sale motives, see Beneish
and Vargus (2002); and for job security and control-related motives, see DeFond and Park (1997),
Ahmed, Lobo, and Zhou (2006), DeFond and Jiambalvo (1994), and Sweeney (1994).

Short Selling and Earnings Management 1257
to inflate the stock price.Prior research also demonstrates that short sell-
ing facilitates the flow of unfavorable information into stock prices, increases
price efficiency,and dampens the price inflation that motivates managers to
manipulate earnings in the first place (e.g., Miller (1977), Harrison and Kreps
(1978), Chang, Cheng, and Yu (2007), Karpoff and Lou (2010), Boehmer and Wu
(2013)). These findings imply that managers’ benefits of manipulating earnings
decrease with the prospect of short selling because these benefits are at least
partially offset by short sellers’activities.
Although earnings management conveys benefits to managers,managers
cannot manipulate earnings with impunity. Previous research shows that ag-
gressive earnings management is associated with an increased likelihood of
forced CEO turnover (Karpoff, Lee, and Martin (2008), Hazarika, Karpoff, and
Nahata (2012)), and that short sellers monitor managers’reporting behavior
and uncover aggressive earnings management (Efendi, Kinney, and Swanson
(2005),Desai, Krishnamurthy, and Venkataraman (2006),Karpoff and Lou
(2010)). These results indicate that, for a given level of earnings management,
managers’potential costs increase with a reduction in the cost of short selling
and an increase in short sellers’scrutiny.
Regulation SHO’s pilot program, which eliminated short-sale price tests for
the pilot stocks,represents an exogenously imposed reduction in the cost of
short selling and hence an increase in the prospect of short selling in these
stocks. The effect was to decrease pilot firm managers’expected benefits and
increase their expected costs of earnings management. These effects on a man-
ager’s earnings management decisions are illustrated in Figure 1. Let MB0 and
MC 0 represent the manager’s marginal benefit and marginal cost of managing
earnings before initiation of the pilot program. In drawing these curves with
their normal slopes,we assume that the benefits from artificialstock price
inflation increase at a decreasing rate in the level of earnings management,
while the costs from the prospect of being discovered increase at an increas-
ing rate. The pre-program optimum amount of earnings management is EM0.
Once the program starts, the marginal benefit and marginal cost of earnings
management shift to MB1 and MC1, and the manager endogenously adjusts by
choosing a new,lower level of earnings management,EM 1. This adjustment
among pilot firms leads to our first hypothesis:
HYPOTHESIS 1: Earnings management in the pilot firms decreases relative to
earnings management in the nonpilot firms during the pilot program.
C. The Impact of the Pilot Program on Fraud Discovery
In developing Hypothesis 1,we assume that the pilot program had a sub-
stantial enough effect on short sellers’ activities to induce a measurable change
in the pilot firms’financial reporting decisions. Previous research finds that,
in general, short selling tracks firms’ discretionary accruals and helps uncover

1258 The Journal of FinanceR
MC1
MC0
MB1
MB0
Marginal
Benefit (MB)
and Marginal
Cost (MC)
Earnings Management
EM0EM1
M
MB0
Upward shift in MC
(from MC 0 to MC1)
Downward shift in MB
(from MB 0 to MB1)
Figure 1. Managers’marginal benefits and marginal costs of earnings management.
This figure illustrates Hypothesis 1,which posits that earnings management in the pilot firms
decreases relative to earnings management in the nonpilot firms during the pilot program. In the
figure, a decrease in the cost of short selling decreases managers’expected benefits from earnings
management and increases their expected costs, leading to a decrease in the optimal amount of
earnings management. Managers’ benefits decrease because the increased prospect of short selling
decreases the potential inflation in stock prices that motivates managers to manage earnings in the
first place. Managers’costs increase because the increased prospect of short selling increases the
probability that the managers will be discovered and face adverse consequences for any given level
of earnings management. These changes result in a downward shift in the marginal benefit and an
upward shift in the marginal cost of earnings management. MB0 and MC0 represent the manager’s
marginal benefits and marginal costs before the decrease in short-selling costs, while MB1 and MC1
represent the marginal benefits and marginal costs after the decrease in short-selling costs.
financial misrepresentation.14 In Section I of the Internet Appendix, we report
results that confirm these two findings in our sample, that is, pilot firms’ short
selling is positively related to their discretionary accruals during the pilot pe-
riod, and short interest increases in months in which firms are later revealed
to have engaged in financial misrepresentation.15 These results are consistent
with the view that the cost reduction induced by the pilot program did pro-
vide sufficient incentives for short sellers to increase their scrutiny of the pilot
firms’ reporting behavior.In this section,we construct a hypothesis and test
for whether the pilot program also increased pilot firms’risk of detection for
14 Desai, Krishnamurthy, and Venkataraman (2006), Cao et al. (2007), Karpoff and Lou (2010),
and Hirshleifer, Teoh, and Yu (2011) report that short selling tracks discretionary accruals.De-
sai, Krishnamurthy, and Venkataraman (2006) find that short selling leads the announcement of
earnings restatements, and Karpoff and Lou (2010) find that short selling accelerates the rate at
which misrepresentation is detected.
15 The Internet Appendix is available in the online version of the article on the Journal of
Finance website.

Short Selling and Earnings Management 1259
earnings manipulation that rises to the level of financial misrepresentation or
fraud.16
We begin by noting that there is generally a time lag between when a firm
begins misrepresenting its earnings and when the misrepresentation is de-
tected.Karpoff and Lou (2010)report that this lag varies across firms and
has a median of 26 months in their sample. We therefore characterize a firm’s
conditional probability of being caught as
Pr (Caught(t + n) |Fraud (t)) = δ n
s=0msSSP (t + s) . (1)
In equation (1), Pr(Caught(t + n)|Fraud(t)) is the firm’s probability of being
caught at time t + n conditional on misrepresenting at time t, where n ࣙ 0. On
the right-hand side, SSP(t + s) is short selling potential at time t + s; we expect
this potential to be higher for pilot firms when t + s falls within the pilot period.
We use ms to denote the individual weight each period’s short-selling potential
contributes to the conditional probability of detection. This weight depends on
the wide range of non short-selling factors that affect a firm’s probability of
being caught. We hypothesize that an increase in short-selling potential helps
uncover aggressive reporting, that is, δ > 0. This leads to our second hypothesis:
HYPOTHESIS 2: Conditional on misreporting,pilot firms are more likely than
nonpilot firms to get caught after the pilot program begins.
A challenge in testing Hypothesis 2 is that we do not directly observe the
conditional probability of detection,but rather the unconditional probability
that a firm both commits fraud and is detected, which can be expressed as
Pr Caught(t + n) , Fraud (t) = Pr (Fraud (t))× Pr Caught(t + n) |Fraud (t) . (2)
To test Hypothesis 2, we exploit the time lag between the commission and
detection of fraud. Since the pilot firms are randomly selected, it is reasonable
to assume that, before the pilot program was announced in July 2004, the actual
rate of fraud commission was equal between the pilot and nonpilot firms, that
is, Pr(Fraud(t))pilot = Pr(Fraud(t))nonpilot for t < July 2004.17 This allows us to
use the unconditional probability of detection for fraud initiated before the pilot
program was announced in July 2004 but detected after the program began in
May 2005 to infer the conditional probability of getting caught. Hypothesis 2
then implies that
Pr (Caught(post-May 2005) , Fraud (pre-July 2004))pilot >
Pr (Caught(post-May 2005) , Fraud (pre-July 2004))nonpilot.
16 Karpoff et al. (2016) point out that many instances of financial misrepresentation do not in-
clude charges of fraud. We nonetheless use the term “fraud” to refer to any illegal misrepresentation
that attracts SEC enforcement action.
17 We restrict t to the period before the announcement of the pilot program (in July 2004) to
ensure that the expected rate of fraud commission is equal across the two groups of firms. Whereas
short sellers arguably begin to change their behavior after the pilot program is implemented in
May 2005, managers of pilot firms could change their reporting behavior in response to the prospect
of short selling as early as when they learn the identity of the pilot stocks in July 2004.

1260 The Journal of FinanceR
Once the pilot program was announced, Hypothesis 1 implies that managers
of the pilot firms endogenously began to adjust to the higher conditional prob-
ability of detection by decreasing earnings management, that is,
Pr (Fraud (t))pilot < Pr (Fraud (t))nonpilot for t > July 2004.
The pilot program therefore has two offsetting effects on the unconditional
probability of detection for fraud committed after July 2004: pilot firms commit
fewer frauds, but conditional on committing fraud, they are more likely to be
caught.This implies that the difference between pilot and nonpilot firms in
the unconditional likelihood of fraud detection should decrease as we consider
fraud initiated after July 2004. Section IV reports results that support these
implications of Hypothesis 2.
D. Related Research
Our investigation is related to the small but growing literature that exploits
changes in short-sale regulations to examine the economic implications of short
selling. Autore, Billingsley, and Kovacs (2011), Frino, Lecce, and Lepone (2011),
and Boehmer, Jones, and Zhang (2013) examine the impact of a widespread ban
on short selling in U.S. equity markets in 2008, and Beber and Pagano (2013)
examine the impacts ofshort-selling bans around the world.These studies
conclude that short-selling bans decrease various measures of market quality.
Using Regulation SHO’s Rule 202T pilot program, Alexander and Peterson
(2008)find that order execution and market quality improved for the pilot
stocks during the pilot program.Diether, Lee, and Werner (2009) and SEC
(2007) show that pilot stocks listed on both NYSE and NASDAQ experienced a
significant increase in short-sale trades and in the ratio of short sales to share
volume during the term of the pilot program. The former also shows that NYSE-
listed pilot stocks experienced a higher level of order-splitting, suggesting that
short sellers apply more active trading strategies.Other papers relate the
pilot program to firm outcomes.Grullon, Michenaud,and Weston (2015),for
example,examine the effect of the pilot program on pilot firms’stock prices,
equity issuance, and investment. Kecsk´es, Mansi, and Zhang (2013) study bond
yields, De Angelis, Grullon, and Michenaud (2015) equity incentives, He and
Tian (2014) corporate innovation,and Li and Zhang (2015) firms’voluntary
disclosure practices.
In our main analyses, we use the experiment created by the pilot program
to examine the effect ofshort-selling costs on firms’earnings management
decisions.This experiment is well suited for our research question, as it
facilitates DiD comparisons of pilot vs. nonpilot firms’earnings management
before, during, and after the pilot program. The DiD tests allow us to control
for time trends that may be common to both the pilot and nonpilot firms,
and mitigate concerns about reverse causality or omitted variables (because
the SEC assigned pilot stocks arbitrarily).This experimental design is thus
superior to a blanket ban of short selling that applies to the entire cross-section

Short Selling and Earnings Management 1261
of firms because the latter can be muddled by possible confounding events. For
example, changes in accruals following the blanket ban on short selling during
the recent financial crisis could be associated with economy-wide changes in
investment opportunities rather than changes in short-selling regulations.
A contemporaneous paper by Massa, Zhang, and Zhang (MZZ, 2015) also in-
vestigates the effects of short selling on firms’ earnings management. Whereas
we use the exogenous variation in firms’ short-selling costs created by the pilot
program to identify our tests, MZZ focus on 33 international markets and use
the amount of shares available for lending to measure short-selling potential.
Like us, MZZ also infer that short selling plays a disciplinary role in deterring
firms’opportunistic reporting behavior.
II. Data
A. Sample
On July 28, 2004, the SEC issued its first pilot order (Securities Exchange
Act Release No.50104) and published a list of 986 stocks that would trade
without being subject to any price tests during the term of the pilot program
(available at http://www.sec.gov/rules/other/34-50104.htm). To create this list,
the SEC started with 2004 Russell 3000 index members and excluded stocks
that were not previously subject to price tests (i.e., not listed on NYSE, Amex,
or NASDAQ-NM) and stocks that went public or had spin-offs after April 30,
2004.The remaining stocks were then sorted by their average daily dollar
volume computed over the June 2003 to May 2004 period within each of the
three listing markets. Every third stock (beginning with the second one) within
each listing market was designated as a pilot stock.
Based on the description in the SEC’s pilot orders and its report on the pilot
program (SEC (2007)),we identify an initial sample of 986 pilot stocks and
1,966 nonpilot stocks.18 An examination of the exchange distribution of these
stocks shows that both the pilot and the nonpilot groups are representative
of the Russell 3000 index,confirming the statistics reported by SEC (2007).
Specifically,of the 986 pilot stocks,49.9% (492) are listed on NYSE,47.9%
(472) on NASDAQ-NM, and 2.2% (22) on Amex. The exchange distribution of
nonpilot stocks is very similar, with 50% (982) listed on NYSE, 48% (944) on
NASDAQ-NM, and 2% (40) on Amex.
In our tests, we delete firms in the financial services (SIC 6000–6999)
and utilities (SIC 4900–4949) industries because disclosure requirements,
18 We use Thomson Reuters’s Securities Data Company (SDC) Platinum database and the Com-
pustat database to identify firms that went public or had spinoffs after April 30,2004,and the
CRSP monthly files to identify stocks that are not exchange-listed,and exclude all such stocks
from the nonpilot sample.The SEC did not publish the final list of nonpilot stocks in its 2007
analysis, but any discrepancies between the SEC’s sample and our sample of nonpilot stocks are
likely to be immaterial. Further, firms that are not exchange-listed or that had significant changes
in ownership structure around the pilot program are likely to be excluded from our tests because
our main tests require that the sample firms have financial data each year from 2001 to 2003
(inclusive) and 2005 to 2010 (inclusive).

1262 The Journal of FinanceR
accounting rules,and processes by which accruals are generated are signif-
icantly different for these regulated industries.A further complication with
financial stocks is the 2008 short-sale ban imposed on this sector.We obtain
data from the Compustat Industrial Annual Files to construct earnings man-
agement proxies and controlvariables.In most tests, we require that firms
have data to calculate firm characteristics over the entire sample period, that
is, 2001 to 2003 (inclusive) and 2005 to 2010 (inclusive). The resulting balanced
panel sample consists of 388 pilot firms and 709 nonpilot firms. If we relax this
requirement, our unbalanced panel sample contains 741 to 782 pilot firms and
1,504 to 1,610 nonpilot firms in the year immediately before the announcement
of the pilot program (i.e., 2003), depending on data availability to calculate a
given firm characteristic.We emphasize the results from the balanced panel
sample,but also report results for the unbalanced sample.Throughout,the
results are similar using either sample.
B. Key Test Variables
We create an indicator variable PILOT to denote firms with pilot stocks
(“pilot firms”). Specifically, PILOT equals one if a firm’s stock is designated as
a pilot stock under Regulation SHO’s pilot program and zero otherwise. Pilot
firms constitute the treatment sample and nonpilot firms serve as the control
sample. The sample period in our main analysis consists of nine calendar
years, 2001 to 2003 (inclusive) and 2005 to 2010 (inclusive). We construct three
variables to indicate three subperiods:PRE equals one if a firm-year’s fiscal
end falls between January 1, 2001 and December 31, 2003 and zero otherwise;
DURING equals one if a firm-year’s fiscal end falls between January 1, 2005
and December 31,2007 and zero otherwise;and POST equals one if a firm-
year’s fiscal end falls between January 1, 2008 and December 31, 2010 and zero
otherwise. We set the subperiods to three calendar years each so it is easier to
align and compare firm financials across periods in the DiD tests,especially
since our outcome variable of interest, earnings quality, exhibits seasonality.
Our during-pilot period, 2005 to 2007, is slightly longer than the course of
the pilot program,which was scheduled to run from May 2,2005 to August
6, 2007 but effectively ran from May 2,2005 to July 6, 2007.This definition
of DURING reflects our assumption that firms’annual reporting outcome is
affected even if the increased prospect of short selling does not extend for the
full year. Table IA.III of the Internet Appendix reports tests that yield similar
results if we instead define the three subperiods as May 2001 to June 2003, May
2005 to June 2007, and May 2008 to June 2010, thus restricting the DURING
period more closely to the actualstart and end dates of the program.Also,
in our primary DiD tests, we omit 2004 because the identity of the pilot and
nonpilot stocks was made public in July 2004, and it is not clear whether 2004
should be classified as part of the pre- or during-pilot period. In Table IA.IV of
the Internet Appendix, we report tests that indicate our main findings are not
substantially affected if we include the entire year of 2004 in the PRE period